Overview

Dataset statistics

Number of variables16
Number of observations17379
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 MiB
Average record size in memory128.0 B

Variable types

Numeric11
Categorical5

Alerts

instant is highly correlated with yrHigh correlation
season is highly correlated with mnthHigh correlation
yr is highly correlated with instantHigh correlation
mnth is highly correlated with seasonHigh correlation
hr is highly correlated with registered and 1 other fieldsHigh correlation
temp is highly correlated with atemp and 1 other fieldsHigh correlation
atemp is highly correlated with temp and 1 other fieldsHigh correlation
casual is highly correlated with temp and 3 other fieldsHigh correlation
registered is highly correlated with hr and 2 other fieldsHigh correlation
cnt is highly correlated with hr and 2 other fieldsHigh correlation
instant is highly correlated with yrHigh correlation
season is highly correlated with mnthHigh correlation
yr is highly correlated with instantHigh correlation
mnth is highly correlated with seasonHigh correlation
temp is highly correlated with atempHigh correlation
atemp is highly correlated with tempHigh correlation
casual is highly correlated with registered and 1 other fieldsHigh correlation
registered is highly correlated with casual and 1 other fieldsHigh correlation
cnt is highly correlated with casual and 1 other fieldsHigh correlation
instant is highly correlated with yrHigh correlation
season is highly correlated with mnthHigh correlation
yr is highly correlated with instantHigh correlation
mnth is highly correlated with seasonHigh correlation
temp is highly correlated with atempHigh correlation
atemp is highly correlated with tempHigh correlation
casual is highly correlated with registered and 1 other fieldsHigh correlation
registered is highly correlated with casual and 1 other fieldsHigh correlation
cnt is highly correlated with casual and 1 other fieldsHigh correlation
instant is highly correlated with season and 4 other fieldsHigh correlation
season is highly correlated with instant and 3 other fieldsHigh correlation
yr is highly correlated with instantHigh correlation
mnth is highly correlated with instant and 3 other fieldsHigh correlation
hr is highly correlated with casual and 2 other fieldsHigh correlation
weekday is highly correlated with workingdayHigh correlation
workingday is highly correlated with weekdayHigh correlation
temp is highly correlated with instant and 4 other fieldsHigh correlation
atemp is highly correlated with instant and 4 other fieldsHigh correlation
casual is highly correlated with hr and 4 other fieldsHigh correlation
registered is highly correlated with hr and 2 other fieldsHigh correlation
cnt is highly correlated with hr and 2 other fieldsHigh correlation
instant is uniformly distributed Uniform
instant has unique values Unique
hr has 726 (4.2%) zeros Zeros
weekday has 2502 (14.4%) zeros Zeros
windspeed has 2180 (12.5%) zeros Zeros
casual has 1581 (9.1%) zeros Zeros

Reproduction

Analysis started2022-09-03 14:27:05.747234
Analysis finished2022-09-03 14:27:27.599744
Duration21.85 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

instant
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct17379
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8690
Minimum1
Maximum17379
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2022-09-03T19:57:27.708020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile869.9
Q14345.5
median8690
Q313034.5
95-th percentile16510.1
Maximum17379
Range17378
Interquartile range (IQR)8689

Descriptive statistics

Standard deviation5017.0295
Coefficient of variation (CV)0.5773336593
Kurtosis-1.2
Mean8690
Median Absolute Deviation (MAD)4345
Skewness0
Sum151023510
Variance25170585
MonotonicityStrictly increasing
2022-09-03T19:57:27.839768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
115921
 
< 0.1%
115781
 
< 0.1%
115791
 
< 0.1%
115801
 
< 0.1%
115811
 
< 0.1%
115821
 
< 0.1%
115831
 
< 0.1%
115841
 
< 0.1%
115851
 
< 0.1%
Other values (17369)17369
99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
173791
< 0.1%
173781
< 0.1%
173771
< 0.1%
173761
< 0.1%
173751
< 0.1%
173741
< 0.1%
173731
< 0.1%
173721
< 0.1%
173711
< 0.1%
173701
< 0.1%

season
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size135.9 KiB
3
4496 
2
4409 
1
4242 
4
4232 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17379
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
34496
25.9%
24409
25.4%
14242
24.4%
44232
24.4%

Length

2022-09-03T19:57:27.966626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-03T19:57:28.099398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
34496
25.9%
24409
25.4%
14242
24.4%
44232
24.4%

Most occurring characters

ValueCountFrequency (%)
34496
25.9%
24409
25.4%
14242
24.4%
44232
24.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17379
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
34496
25.9%
24409
25.4%
14242
24.4%
44232
24.4%

Most occurring scripts

ValueCountFrequency (%)
Common17379
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
34496
25.9%
24409
25.4%
14242
24.4%
44232
24.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII17379
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
34496
25.9%
24409
25.4%
14242
24.4%
44232
24.4%

yr
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size135.9 KiB
1
8734 
0
8645 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17379
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
18734
50.3%
08645
49.7%

Length

2022-09-03T19:57:28.203429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-03T19:57:28.316150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
18734
50.3%
08645
49.7%

Most occurring characters

ValueCountFrequency (%)
18734
50.3%
08645
49.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17379
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
18734
50.3%
08645
49.7%

Most occurring scripts

ValueCountFrequency (%)
Common17379
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
18734
50.3%
08645
49.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII17379
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18734
50.3%
08645
49.7%

mnth
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.537775476
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2022-09-03T19:57:28.399811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.438775714
Coefficient of variation (CV)0.5259855935
Kurtosis-1.201878197
Mean6.537775476
Median Absolute Deviation (MAD)3
Skewness-0.009253248383
Sum113620
Variance11.82517841
MonotonicityNot monotonic
2022-09-03T19:57:28.493538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
51488
8.6%
71488
8.6%
121483
8.5%
81475
8.5%
31473
8.5%
101451
8.3%
61440
8.3%
41437
8.3%
91437
8.3%
111437
8.3%
Other values (2)2770
15.9%
ValueCountFrequency (%)
11429
8.2%
21341
7.7%
31473
8.5%
41437
8.3%
51488
8.6%
61440
8.3%
71488
8.6%
81475
8.5%
91437
8.3%
101451
8.3%
ValueCountFrequency (%)
121483
8.5%
111437
8.3%
101451
8.3%
91437
8.3%
81475
8.5%
71488
8.6%
61440
8.3%
51488
8.6%
41437
8.3%
31473
8.5%

hr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.54675183
Minimum0
Maximum23
Zeros726
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2022-09-03T19:57:28.626075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.914405095
Coefficient of variation (CV)0.5988181957
Kurtosis-1.198020588
Mean11.54675183
Median Absolute Deviation (MAD)6
Skewness-0.01067990952
Sum200671
Variance47.80899782
MonotonicityNot monotonic
2022-09-03T19:57:28.754713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
17730
 
4.2%
16730
 
4.2%
13729
 
4.2%
15729
 
4.2%
14729
 
4.2%
12728
 
4.2%
22728
 
4.2%
21728
 
4.2%
20728
 
4.2%
19728
 
4.2%
Other values (14)10092
58.1%
ValueCountFrequency (%)
0726
4.2%
1724
4.2%
2715
4.1%
3697
4.0%
4697
4.0%
5717
4.1%
6725
4.2%
7727
4.2%
8727
4.2%
9727
4.2%
ValueCountFrequency (%)
23728
4.2%
22728
4.2%
21728
4.2%
20728
4.2%
19728
4.2%
18728
4.2%
17730
4.2%
16730
4.2%
15729
4.2%
14729
4.2%

holiday
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size135.9 KiB
0
16879 
1
 
500

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17379
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
016879
97.1%
1500
 
2.9%

Length

2022-09-03T19:57:28.887326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-03T19:57:29.008517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
016879
97.1%
1500
 
2.9%

Most occurring characters

ValueCountFrequency (%)
016879
97.1%
1500
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17379
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
016879
97.1%
1500
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common17379
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
016879
97.1%
1500
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII17379
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
016879
97.1%
1500
 
2.9%

weekday
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.003682605
Minimum0
Maximum6
Zeros2502
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2022-09-03T19:57:29.099410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.005771456
Coefficient of variation (CV)0.6677707733
Kurtosis-1.255996891
Mean3.003682605
Median Absolute Deviation (MAD)2
Skewness-0.002998221376
Sum52201
Variance4.023119134
MonotonicityNot monotonic
2022-09-03T19:57:29.206189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
62512
14.5%
02502
14.4%
52487
14.3%
12479
14.3%
32475
14.2%
42471
14.2%
22453
14.1%
ValueCountFrequency (%)
02502
14.4%
12479
14.3%
22453
14.1%
32475
14.2%
42471
14.2%
52487
14.3%
62512
14.5%
ValueCountFrequency (%)
62512
14.5%
52487
14.3%
42471
14.2%
32475
14.2%
22453
14.1%
12479
14.3%
02502
14.4%

workingday
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size135.9 KiB
1
11865 
0
5514 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17379
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
111865
68.3%
05514
31.7%

Length

2022-09-03T19:57:29.325392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-03T19:57:29.446577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
111865
68.3%
05514
31.7%

Most occurring characters

ValueCountFrequency (%)
111865
68.3%
05514
31.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17379
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
111865
68.3%
05514
31.7%

Most occurring scripts

ValueCountFrequency (%)
Common17379
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
111865
68.3%
05514
31.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII17379
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
111865
68.3%
05514
31.7%

weathersit
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size135.9 KiB
1
11413 
2
4544 
3
1419 
4
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17379
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
111413
65.7%
24544
 
26.1%
31419
 
8.2%
43
 
< 0.1%

Length

2022-09-03T19:57:29.551871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-03T19:57:29.750042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
111413
65.7%
24544
 
26.1%
31419
 
8.2%
43
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
111413
65.7%
24544
 
26.1%
31419
 
8.2%
43
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17379
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
111413
65.7%
24544
 
26.1%
31419
 
8.2%
43
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common17379
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
111413
65.7%
24544
 
26.1%
31419
 
8.2%
43
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII17379
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
111413
65.7%
24544
 
26.1%
31419
 
8.2%
43
 
< 0.1%

temp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct50
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4969871684
Minimum0.02
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2022-09-03T19:57:29.958148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.2
Q10.34
median0.5
Q30.66
95-th percentile0.8
Maximum1
Range0.98
Interquartile range (IQR)0.32

Descriptive statistics

Standard deviation0.1925561212
Coefficient of variation (CV)0.3874468668
Kurtosis-0.9418442041
Mean0.4969871684
Median Absolute Deviation (MAD)0.16
Skewness-0.006020883348
Sum8637.14
Variance0.03707785983
MonotonicityNot monotonic
2022-09-03T19:57:30.180165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.62726
 
4.2%
0.66693
 
4.0%
0.64692
 
4.0%
0.7690
 
4.0%
0.6675
 
3.9%
0.36671
 
3.9%
0.34645
 
3.7%
0.3641
 
3.7%
0.4614
 
3.5%
0.32611
 
3.5%
Other values (40)10721
61.7%
ValueCountFrequency (%)
0.0217
 
0.1%
0.0416
 
0.1%
0.0616
 
0.1%
0.0817
 
0.1%
0.151
 
0.3%
0.1276
 
0.4%
0.14138
 
0.8%
0.16230
1.3%
0.18155
0.9%
0.2354
2.0%
ValueCountFrequency (%)
11
 
< 0.1%
0.981
 
< 0.1%
0.9616
 
0.1%
0.9417
 
0.1%
0.9249
 
0.3%
0.990
0.5%
0.8853
 
0.3%
0.86131
0.8%
0.84138
0.8%
0.82213
1.2%

atemp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct65
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4757751021
Minimum0
Maximum1
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2022-09-03T19:57:30.391742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2121
Q10.3333
median0.4848
Q30.6212
95-th percentile0.7424
Maximum1
Range1
Interquartile range (IQR)0.2879

Descriptive statistics

Standard deviation0.1718502156
Coefficient of variation (CV)0.3612005228
Kurtosis-0.8454118948
Mean0.4757751021
Median Absolute Deviation (MAD)0.1364
Skewness-0.09042885856
Sum8268.4955
Variance0.02953249661
MonotonicityNot monotonic
2022-09-03T19:57:30.651997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6212988
 
5.7%
0.5152618
 
3.6%
0.4091614
 
3.5%
0.3333600
 
3.5%
0.6667593
 
3.4%
0.6061588
 
3.4%
0.5303579
 
3.3%
0.5575
 
3.3%
0.4545559
 
3.2%
0.303549
 
3.2%
Other values (55)11116
64.0%
ValueCountFrequency (%)
02
 
< 0.1%
0.01524
 
< 0.1%
0.03038
 
< 0.1%
0.04559
 
0.1%
0.060614
 
0.1%
0.075828
 
0.2%
0.090913
 
0.1%
0.106135
 
0.2%
0.121286
0.5%
0.136490
0.5%
ValueCountFrequency (%)
11
 
< 0.1%
0.98482
 
< 0.1%
0.95451
 
< 0.1%
0.92425
 
< 0.1%
0.90915
 
< 0.1%
0.893915
 
0.1%
0.878819
0.1%
0.863620
0.1%
0.848532
0.2%
0.833341
0.2%

hum
Real number (ℝ≥0)

Distinct89
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6272288394
Minimum0
Maximum1
Zeros22
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2022-09-03T19:57:30.799508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.31
Q10.48
median0.63
Q30.78
95-th percentile0.93
Maximum1
Range1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.1929298341
Coefficient of variation (CV)0.3075908216
Kurtosis-0.8261167359
Mean0.6272288394
Median Absolute Deviation (MAD)0.15
Skewness-0.1112871494
Sum10900.61
Variance0.03722192087
MonotonicityNot monotonic
2022-09-03T19:57:30.935707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.88657
 
3.8%
0.83630
 
3.6%
0.94560
 
3.2%
0.87488
 
2.8%
0.7430
 
2.5%
0.66388
 
2.2%
0.65387
 
2.2%
0.69359
 
2.1%
0.55352
 
2.0%
0.74341
 
2.0%
Other values (79)12787
73.6%
ValueCountFrequency (%)
022
0.1%
0.081
 
< 0.1%
0.11
 
< 0.1%
0.121
 
< 0.1%
0.131
 
< 0.1%
0.142
 
< 0.1%
0.154
 
< 0.1%
0.1610
0.1%
0.1710
0.1%
0.1810
0.1%
ValueCountFrequency (%)
1270
1.6%
0.971
 
< 0.1%
0.963
 
< 0.1%
0.94560
3.2%
0.93331
1.9%
0.922
 
< 0.1%
0.911
 
< 0.1%
0.97
 
< 0.1%
0.89239
 
1.4%
0.88657
3.8%

windspeed
Real number (ℝ≥0)

ZEROS

Distinct30
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1900976063
Minimum0
Maximum0.8507
Zeros2180
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2022-09-03T19:57:31.065187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1045
median0.194
Q30.2537
95-th percentile0.4179
Maximum0.8507
Range0.8507
Interquartile range (IQR)0.1492

Descriptive statistics

Standard deviation0.1223402286
Coefficient of variation (CV)0.6435653291
Kurtosis0.5908204107
Mean0.1900976063
Median Absolute Deviation (MAD)0.0895
Skewness0.5749052035
Sum3303.7063
Variance0.01496713153
MonotonicityNot monotonic
2022-09-03T19:57:31.174992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
02180
12.5%
0.13431738
10.0%
0.16421695
9.8%
0.1941657
9.5%
0.10451617
9.3%
0.22391513
8.7%
0.08961425
8.2%
0.25371295
7.5%
0.28361048
6.0%
0.2985808
 
4.6%
Other values (20)2403
13.8%
ValueCountFrequency (%)
02180
12.5%
0.08961425
8.2%
0.10451617
9.3%
0.13431738
10.0%
0.16421695
9.8%
0.1941657
9.5%
0.22391513
8.7%
0.25371295
7.5%
0.28361048
6.0%
0.2985808
 
4.6%
ValueCountFrequency (%)
0.85072
 
< 0.1%
0.83581
 
< 0.1%
0.8062
 
< 0.1%
0.77611
 
< 0.1%
0.74632
 
< 0.1%
0.71642
 
< 0.1%
0.68665
 
< 0.1%
0.656711
0.1%
0.641814
0.1%
0.611923
0.1%

casual
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct322
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.67621842
Minimum0
Maximum367
Zeros1581
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2022-09-03T19:57:31.301477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median17
Q348
95-th percentile138.1
Maximum367
Range367
Interquartile range (IQR)44

Descriptive statistics

Standard deviation49.30503039
Coefficient of variation (CV)1.382013918
Kurtosis7.571001747
Mean35.67621842
Median Absolute Deviation (MAD)16
Skewness2.499236891
Sum620017
Variance2430.986021
MonotonicityNot monotonic
2022-09-03T19:57:31.435428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01581
 
9.1%
11082
 
6.2%
2798
 
4.6%
3697
 
4.0%
4561
 
3.2%
5509
 
2.9%
6448
 
2.6%
7405
 
2.3%
8377
 
2.2%
9348
 
2.0%
Other values (312)10573
60.8%
ValueCountFrequency (%)
01581
9.1%
11082
6.2%
2798
4.6%
3697
4.0%
4561
 
3.2%
5509
 
2.9%
6448
 
2.6%
7405
 
2.3%
8377
 
2.2%
9348
 
2.0%
ValueCountFrequency (%)
3671
< 0.1%
3621
< 0.1%
3611
< 0.1%
3571
< 0.1%
3561
< 0.1%
3551
< 0.1%
3541
< 0.1%
3521
< 0.1%
3501
< 0.1%
3471
< 0.1%

registered
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct776
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.7868692
Minimum0
Maximum886
Zeros24
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2022-09-03T19:57:31.579581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q134
median115
Q3220
95-th percentile465
Maximum886
Range886
Interquartile range (IQR)186

Descriptive statistics

Standard deviation151.3572859
Coefficient of variation (CV)0.9842016207
Kurtosis2.750017757
Mean153.7868692
Median Absolute Deviation (MAD)89
Skewness1.557904226
Sum2672662
Variance22909.028
MonotonicityNot monotonic
2022-09-03T19:57:32.020627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4307
 
1.8%
3294
 
1.7%
5287
 
1.7%
6266
 
1.5%
2245
 
1.4%
1201
 
1.2%
7200
 
1.2%
8190
 
1.1%
9178
 
1.0%
11140
 
0.8%
Other values (766)15071
86.7%
ValueCountFrequency (%)
024
 
0.1%
1201
1.2%
2245
1.4%
3294
1.7%
4307
1.8%
5287
1.7%
6266
1.5%
7200
1.2%
8190
1.1%
9178
1.0%
ValueCountFrequency (%)
8861
< 0.1%
8851
< 0.1%
8762
< 0.1%
8711
< 0.1%
8601
< 0.1%
8572
< 0.1%
8391
< 0.1%
8381
< 0.1%
8331
< 0.1%
8221
< 0.1%

cnt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct869
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189.4630876
Minimum1
Maximum977
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2022-09-03T19:57:32.167640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q140
median142
Q3281
95-th percentile563.1
Maximum977
Range976
Interquartile range (IQR)241

Descriptive statistics

Standard deviation181.3875991
Coefficient of variation (CV)0.9573769823
Kurtosis1.417203281
Mean189.4630876
Median Absolute Deviation (MAD)112
Skewness1.277411604
Sum3292679
Variance32901.4611
MonotonicityNot monotonic
2022-09-03T19:57:32.311086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5260
 
1.5%
6236
 
1.4%
4231
 
1.3%
3224
 
1.3%
2208
 
1.2%
7198
 
1.1%
8182
 
1.0%
1158
 
0.9%
10155
 
0.9%
11147
 
0.8%
Other values (859)15380
88.5%
ValueCountFrequency (%)
1158
0.9%
2208
1.2%
3224
1.3%
4231
1.3%
5260
1.5%
6236
1.4%
7198
1.1%
8182
1.0%
9128
0.7%
10155
0.9%
ValueCountFrequency (%)
9771
< 0.1%
9761
< 0.1%
9701
< 0.1%
9681
< 0.1%
9671
< 0.1%
9631
< 0.1%
9571
< 0.1%
9531
< 0.1%
9481
< 0.1%
9431
< 0.1%

Interactions

2022-09-03T19:57:25.494016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:11.168623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:12.540831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:13.857468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:15.724288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:17.051769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:18.425137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:19.718152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:21.037011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:22.373279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:24.028004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:25.629503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:11.307251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:12.658287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:13.981264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:15.848258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:17.227113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:18.580254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:19.849778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:21.168139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:22.503413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:24.167388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:25.812349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:11.424875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:12.768880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:14.087187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:15.968508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:17.344955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:18.696676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:19.961018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:21.293787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:22.621616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:24.299992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:25.923658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:11.546629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:12.879709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:14.190226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:16.081999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:17.449771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:18.811668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:20.065276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:21.403098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:22.732877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:24.424656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:26.015895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:11.664197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:12.991457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:14.300915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:16.218125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:17.570091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:18.931895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:20.186293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:21.530203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:22.874530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:24.572170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:26.126944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:11.780625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:13.112799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:14.413837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:16.341311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:17.687693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:19.049012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:20.312373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:21.642956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:22.994730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:24.711234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:26.240412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:11.895133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:13.255039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:14.521484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:16.455914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:17.806230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:19.166831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:20.425112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:21.762688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:23.111938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:24.866201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:26.334153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:12.013031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:13.370017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:15.267807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:16.569577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:17.934125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:19.281138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:20.551764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:21.891892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:23.228965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:24.994834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:26.468132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:12.131426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:13.478904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:15.385397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:16.680202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:18.062445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:19.387671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:20.673446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:22.005136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:23.346883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:25.117663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:26.689976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:12.253556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:13.604777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:15.507009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:16.800173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:18.190108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:19.504169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:20.796643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:22.134363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:23.780746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:25.246302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:26.883212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:12.383379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:13.727613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:15.617983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:16.926114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:18.307932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:19.610409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:20.911363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:22.256029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:23.902348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-03T19:57:25.375312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-09-03T19:57:32.450015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-03T19:57:32.651900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-03T19:57:32.856079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-03T19:57:33.049731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-03T19:57:33.203273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-03T19:57:27.113667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-03T19:57:27.453580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

instantseasonyrmnthhrholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcnt
01101006010.240.28790.810.000031316
12101106010.220.27270.800.000083240
23101206010.220.27270.800.000052732
34101306010.240.28790.750.000031013
45101406010.240.28790.750.0000011
56101506020.240.25760.750.0896011
67101606010.220.27270.800.0000202
78101706010.200.25760.860.0000123
89101806010.240.28790.750.0000178
910101906010.320.34850.760.00008614

Last rows

instantseasonyrmnthhrholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcnt
173691737011121401120.280.27270.450.223962185247
173701737111121501120.280.28790.450.134369246315
173711737211121601120.260.25760.480.194030184214
173721737311121701120.260.28790.480.089614150164
173731737411121801120.260.27270.480.134310112122
173741737511121901120.260.25760.600.164211108119
173751737611122001120.260.25760.600.164288189
173761737711122101110.260.25760.600.164278390
173771737811122201110.260.27270.560.1343134861
173781737911122301110.260.27270.650.1343123749